Tensorflow

1@sock.route('/api')
2def echo(sock):
3    while True:
4        # get user input from browser
5        user_input = sock.receive()
6        # print user input on console
7        print(user_input)
8        # read answer from console
9        response = input()
10        # send response to browser
11        sock.send(response)
12

1@sock.route('/api')
2def echo(sock):
3    while True:
4        # get user input from browser
5        user_input = sock.receive()
6        # print user input on console
7        print(user_input)
8        # read answer from console
9        response = input()
10        # send response to browser
11        sock.send(response)
12while True:
13    question = input("")
14    ints = predict(question)
15    answer = response(ints, json_data)
16    print(answer)
17

1@sock.route('/api')
2def echo(sock):
3    while True:
4        # get user input from browser
5        user_input = sock.receive()
6        # print user input on console
7        print(user_input)
8        # read answer from console
9        response = input()
10        # send response to browser
11        sock.send(response)
12while True:
13    question = input("")
14    ints = predict(question)
15    answer = response(ints, json_data)
16    print(answer)
17def predict(sentence):
18    bag_of_words = convert_sentence_in_bag_of_words(sentence)
19    # pass bag as list and get index 0
20    prediction = model.predict(np.array([bag_of_words]))[0]
21    ERROR_THRESHOLD = 0.25
22    accepted_results = [[tag, probability] for tag, probability in enumerate(prediction) if probability > ERROR_THRESHOLD]
23
24    accepted_results.sort(key=lambda x: x[1], reverse=True)
25
26    output = []
27    for accepted_result in accepted_results:
28        output.append({'intent': classes[accepted_result[0]], 'probability': str(accepted_result[1])})
29        print(output)
30    return output
31
32
33def response(intents, json):
34    tag = intents[0]['intent']
35    intents_as_list = json['intents']
36    for i in intents_as_list:
37        if i['tag'] == tag:
38            res = random.choice(i['responses'])
39            break
40    return res
41

1@sock.route('/api')
2def echo(sock):
3    while True:
4        # get user input from browser
5        user_input = sock.receive()
6        # print user input on console
7        print(user_input)
8        # read answer from console
9        response = input()
10        # send response to browser
11        sock.send(response)
12while True:
13    question = input("")
14    ints = predict(question)
15    answer = response(ints, json_data)
16    print(answer)
17def predict(sentence):
18    bag_of_words = convert_sentence_in_bag_of_words(sentence)
19    # pass bag as list and get index 0
20    prediction = model.predict(np.array([bag_of_words]))[0]
21    ERROR_THRESHOLD = 0.25
22    accepted_results = [[tag, probability] for tag, probability in enumerate(prediction) if probability > ERROR_THRESHOLD]
23
24    accepted_results.sort(key=lambda x: x[1], reverse=True)
25
26    output = []
27    for accepted_result in accepted_results:
28        output.append({'intent': classes[accepted_result[0]], 'probability': str(accepted_result[1])})
29        print(output)
30    return output
31
32
33def response(intents, json):
34    tag = intents[0]['intent']
35    intents_as_list = json['intents']
36    for i in intents_as_list:
37        if i['tag'] == tag:
38            res = random.choice(i['responses'])
39            break
40    return res
41 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
42 * Restarting with stat
43 * Serving Flask app 'server' (lazy loading)
44 * Environment: production
45   WARNING: This is a development server. Do not use it in a production deployment.
46   Use a production WSGI server instead.
47 * Debug mode: on
48

1@sock.route('/api')
2def echo(sock):
3    while True:
4        # get user input from browser
5        user_input = sock.receive()
6        # print user input on console
7        print(user_input)
8        # read answer from console
9        response = input()
10        # send response to browser
11        sock.send(response)
12while True:
13    question = input("")
14    ints = predict(question)
15    answer = response(ints, json_data)
16    print(answer)
17def predict(sentence):
18    bag_of_words = convert_sentence_in_bag_of_words(sentence)
19    # pass bag as list and get index 0
20    prediction = model.predict(np.array([bag_of_words]))[0]
21    ERROR_THRESHOLD = 0.25
22    accepted_results = [[tag, probability] for tag, probability in enumerate(prediction) if probability > ERROR_THRESHOLD]
23
24    accepted_results.sort(key=lambda x: x[1], reverse=True)
25
26    output = []
27    for accepted_result in accepted_results:
28        output.append({'intent': classes[accepted_result[0]], 'probability': str(accepted_result[1])})
29        print(output)
30    return output
31
32
33def response(intents, json):
34    tag = intents[0]['intent']
35    intents_as_list = json['intents']
36    for i in intents_as_list:
37        if i['tag'] == tag:
38            res = random.choice(i['responses'])
39            break
40    return res
41 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
42 * Restarting with stat
43 * Serving Flask app 'server' (lazy loading)
44 * Environment: production
45   WARNING: This is a development server. Do not use it in a production deployment.
46   Use a production WSGI server instead.
47 * Debug mode: on
482022-02-13 11:31:38.887640: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
492022-02-13 11:31:38.887734: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
50Metal device set to: Apple M1
51
52systemMemory: 16.00 GB
53maxCacheSize: 5.33 GB
54

1plugins {
2    id 'com.android.application'
3    id 'kotlin-android'
4}
5
6android {
7    compileSdkVersion 30
8    buildToolsVersion "30.0.3"
9
10    defaultConfig {
11        applicationId "com.example.retrofit_test"
12        minSdkVersion 21
13        targetSdkVersion 30
14        versionCode 1
15        versionName "1.0"
16
17        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
18    }
19
20    buildTypes {
21        release {
22            minifyEnabled false
23            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
24        }
25    }
26    compileOptions {
27        sourceCompatibility JavaVersion.VERSION_1_8
28        targetCompatibility JavaVersion.VERSION_1_8
29    }
30    kotlinOptions {
31        jvmTarget = '1.8'
32    }
33}
34
35dependencies {
36
37//    implementation 'com.google.guava:guava:30.1.1-jre'
38    implementation 'com.google.guava:guava:30.1-jre'
39
40//    implementation 'org.jetbrains.kotlin:kotlin-stdlib:1.5.30-M1'
41
42    implementation 'androidx.core:core-ktx:1.6.0'
43    implementation 'androidx.appcompat:appcompat:1.3.1'
44    implementation 'com.google.android.material:material:1.4.0'
45    implementation 'androidx.constraintlayout:constraintlayout:2.1.0'
46    implementation 'androidx.navigation:navigation-fragment-ktx:2.3.5'
47    implementation 'androidx.navigation:navigation-ui-ktx:2.3.5'
48    implementation 'androidx.lifecycle:lifecycle-livedata-ktx:2.3.1'
49    implementation 'androidx.lifecycle:lifecycle-viewmodel-ktx:2.3.1'
50    implementation 'androidx.navigation:navigation-fragment-ktx:2.3.5'
51    implementation 'androidx.navigation:navigation-ui-ktx:2.3.5'
52
53    implementation 'com.squareup.retrofit2:retrofit:2.9.0'
54    implementation 'com.google.code.gson:gson:2.8.7'
55    implementation 'com.squareup.retrofit2:converter-gson:2.9.0'
56    implementation 'com.squareup.okhttp3:logging-interceptor:4.9.1'
57
58    implementation 'com.github.bumptech.glide:glide:4.12.0'
59    implementation 'android.arch.persistence.room:guava:1.1.1'
60    annotationProcessor 'com.github.bumptech.glide:compiler:4.11.0'
61
62    testImplementation 'junit:junit:4.13.2'
63    androidTestImplementation 'androidx.test.ext:junit:1.1.3'
64    androidTestImplementation 'androidx.test.espresso:espresso-core:3.4.0'
65}
66

1plugins {
2    id 'com.android.application'
3    id 'kotlin-android'
4}
5
6android {
7    compileSdkVersion 30
8    buildToolsVersion "30.0.3"
9
10    defaultConfig {
11        applicationId "com.example.retrofit_test"
12        minSdkVersion 21
13        targetSdkVersion 30
14        versionCode 1
15        versionName "1.0"
16
17        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
18    }
19
20    buildTypes {
21        release {
22            minifyEnabled false
23            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
24        }
25    }
26    compileOptions {
27        sourceCompatibility JavaVersion.VERSION_1_8
28        targetCompatibility JavaVersion.VERSION_1_8
29    }
30    kotlinOptions {
31        jvmTarget = '1.8'
32    }
33}
34
35dependencies {
36
37//    implementation 'com.google.guava:guava:30.1.1-jre'
38    implementation 'com.google.guava:guava:30.1-jre'
39
40//    implementation 'org.jetbrains.kotlin:kotlin-stdlib:1.5.30-M1'
41
42    implementation 'androidx.core:core-ktx:1.6.0'
43    implementation 'androidx.appcompat:appcompat:1.3.1'
44    implementation 'com.google.android.material:material:1.4.0'
45    implementation 'androidx.constraintlayout:constraintlayout:2.1.0'
46    implementation 'androidx.navigation:navigation-fragment-ktx:2.3.5'
47    implementation 'androidx.navigation:navigation-ui-ktx:2.3.5'
48    implementation 'androidx.lifecycle:lifecycle-livedata-ktx:2.3.1'
49    implementation 'androidx.lifecycle:lifecycle-viewmodel-ktx:2.3.1'
50    implementation 'androidx.navigation:navigation-fragment-ktx:2.3.5'
51    implementation 'androidx.navigation:navigation-ui-ktx:2.3.5'
52
53    implementation 'com.squareup.retrofit2:retrofit:2.9.0'
54    implementation 'com.google.code.gson:gson:2.8.7'
55    implementation 'com.squareup.retrofit2:converter-gson:2.9.0'
56    implementation 'com.squareup.okhttp3:logging-interceptor:4.9.1'
57
58    implementation 'com.github.bumptech.glide:glide:4.12.0'
59    implementation 'android.arch.persistence.room:guava:1.1.1'
60    annotationProcessor 'com.github.bumptech.glide:compiler:4.11.0'
61
62    testImplementation 'junit:junit:4.13.2'
63    androidTestImplementation 'androidx.test.ext:junit:1.1.3'
64    androidTestImplementation 'androidx.test.espresso:espresso-core:3.4.0'
65}
66   Could not resolve all artifacts for configuration ':classpath'.
67   Could not find org.jetbrains.kotlin:kotlin-gradle-plugin:1.5.30.
68     Searched in the following locations:
69       - https://dl.google.com/dl/android/maven2/org/jetbrains/kotlin/kotlin-gradle-plugin/1.5.30/kotlin-gradle-plugin-1.5.30.pom
70     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
71     Required by:
72         project :
73   Could not find org.jetbrains.kotlin:kotlin-stdlib-jdk8:1.4.32.
74     Searched in the following locations:
75       - https://dl.google.com/dl/android/maven2/org/jetbrains/kotlin/kotlin-stdlib-jdk8/1.4.32/kotlin-stdlib-jdk8-1.4.32.pom
76     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
77     Required by:
78         project : > com.android.tools.build:gradle:7.0.2
79         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
80         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:repository:30.0.2
81         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:aaptcompiler:7.0.2
82         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.analytics-library:shared:30.0.2
83         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.lint:lint-model:30.0.2
84         project : > com.android.tools.build:gradle:7.0.2 > androidx.databinding:databinding-compiler-common:7.0.2
85         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-host-retention-proto:30.0.2
86         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
87         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder-model:7.0.2
88         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:gradle-api:7.0.2
89         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2 > com.android.tools:common:30.0.2
90         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.analytics-library:tracker:30.0.2
91         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.build:manifest-merger:30.0.2
92   Could not find org.apache.httpcomponents:httpmime:4.5.6.
93     Searched in the following locations:
94       - https://dl.google.com/dl/android/maven2/org/apache/httpcomponents/httpmime/4.5.6/httpmime-4.5.6.pom
95     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
96     Required by:
97         project : > com.android.tools.build:gradle:7.0.2
98         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdklib:30.0.2
99         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.analytics-library:crash:30.0.2
100   Could not find commons-io:commons-io:2.4.
101     Searched in the following locations:
102       - https://dl.google.com/dl/android/maven2/commons-io/commons-io/2.4/commons-io-2.4.pom
103     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
104     Required by:
105         project : > com.android.tools.build:gradle:7.0.2
106         project : > com.android.tools.build:gradle:7.0.2 > androidx.databinding:databinding-compiler-common:7.0.2
107   Could not find org.ow2.asm:asm:7.0.
108     Searched in the following locations:
109       - https://dl.google.com/dl/android/maven2/org/ow2/asm/asm/7.0/asm-7.0.pom
110     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
111     Required by:
112         project : > com.android.tools.build:gradle:7.0.2
113         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
114         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:gradle-api:7.0.2
115   Could not find org.ow2.asm:asm-analysis:7.0.
116     Searched in the following locations:
117       - https://dl.google.com/dl/android/maven2/org/ow2/asm/asm-analysis/7.0/asm-analysis-7.0.pom
118     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
119     Required by:
120         project : > com.android.tools.build:gradle:7.0.2
121   Could not find org.ow2.asm:asm-commons:7.0.
122     Searched in the following locations:
123       - https://dl.google.com/dl/android/maven2/org/ow2/asm/asm-commons/7.0/asm-commons-7.0.pom
124     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
125     Required by:
126         project : > com.android.tools.build:gradle:7.0.2
127         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
128   Could not find org.ow2.asm:asm-util:7.0.
129     Searched in the following locations:
130       - https://dl.google.com/dl/android/maven2/org/ow2/asm/asm-util/7.0/asm-util-7.0.pom
131     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
132     Required by:
133         project : > com.android.tools.build:gradle:7.0.2
134         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
135   Could not find org.bouncycastle:bcpkix-jdk15on:1.56.
136     Searched in the following locations:
137       - https://dl.google.com/dl/android/maven2/org/bouncycastle/bcpkix-jdk15on/1.56/bcpkix-jdk15on-1.56.pom
138     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
139     Required by:
140         project : > com.android.tools.build:gradle:7.0.2
141         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
142         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
143         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.build:apkzlib:7.0.2
144   Could not find org.glassfish.jaxb:jaxb-runtime:2.3.2.
145     Searched in the following locations:
146       - https://dl.google.com/dl/android/maven2/org/glassfish/jaxb/jaxb-runtime/2.3.2/jaxb-runtime-2.3.2.pom
147     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
148     Required by:
149         project : > com.android.tools.build:gradle:7.0.2
150         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
151         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdklib:30.0.2
152         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:repository:30.0.2
153         project : > com.android.tools.build:gradle:7.0.2 > androidx.databinding:databinding-compiler-common:7.0.2
154   Could not find net.sf.jopt-simple:jopt-simple:4.9.
155     Searched in the following locations:
156       - https://dl.google.com/dl/android/maven2/net/sf/jopt-simple/jopt-simple/4.9/jopt-simple-4.9.pom
157     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
158     Required by:
159         project : > com.android.tools.build:gradle:7.0.2
160         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
161   Could not find com.squareup:javapoet:1.10.0.
162     Searched in the following locations:
163       - https://dl.google.com/dl/android/maven2/com/squareup/javapoet/1.10.0/javapoet-1.10.0.pom
164     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
165     Required by:
166         project : > com.android.tools.build:gradle:7.0.2
167         project : > com.android.tools.build:gradle:7.0.2 > androidx.databinding:databinding-compiler-common:7.0.2
168   Could not find com.google.protobuf:protobuf-java:3.10.0.
169     Searched in the following locations:
170       - https://dl.google.com/dl/android/maven2/com/google/protobuf/protobuf-java/3.10.0/protobuf-java-3.10.0.pom
171     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
172     Required by:
173         project : > com.android.tools.build:gradle:7.0.2
174         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
175         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.ddms:ddmlib:30.0.2
176         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:aapt2-proto:7.0.2-7396180
177         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:aaptcompiler:7.0.2
178         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-device-provider-gradle-proto:30.0.2
179         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-host-retention-proto:30.0.2
180         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
181         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:bundletool:1.6.0
182         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.analytics-library:shared:30.0.2 > com.android.tools.analytics-library:protos:30.0.2
183         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.analytics-library:tracker:30.0.2
184   Could not find com.google.protobuf:protobuf-java-util:3.10.0.
185     Searched in the following locations:
186       - https://dl.google.com/dl/android/maven2/com/google/protobuf/protobuf-java-util/3.10.0/protobuf-java-util-3.10.0.pom
187     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
188     Required by:
189         project : > com.android.tools.build:gradle:7.0.2
190         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:bundletool:1.6.0
191   Could not find com.google.code.gson:gson:2.8.6.
192     Searched in the following locations:
193       - https://dl.google.com/dl/android/maven2/com/google/code/gson/gson/2.8.6/gson-2.8.6.pom
194     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
195     Required by:
196         project : > com.android.tools.build:gradle:7.0.2
197         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
198         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdklib:30.0.2
199         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.analytics-library:shared:30.0.2
200         project : > com.android.tools.build:gradle:7.0.2 > androidx.databinding:databinding-compiler-common:7.0.2
201         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
202         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.build:manifest-merger:30.0.2
203   Could not find io.grpc:grpc-core:1.21.1.
204     Searched in the following locations:
205       - https://dl.google.com/dl/android/maven2/io/grpc/grpc-core/1.21.1/grpc-core-1.21.1.pom
206     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
207     Required by:
208         project : > com.android.tools.build:gradle:7.0.2
209         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
210   Could not find io.grpc:grpc-netty:1.21.1.
211     Searched in the following locations:
212       - https://dl.google.com/dl/android/maven2/io/grpc/grpc-netty/1.21.1/grpc-netty-1.21.1.pom
213     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
214     Required by:
215         project : > com.android.tools.build:gradle:7.0.2
216         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
217   Could not find io.grpc:grpc-protobuf:1.21.1.
218     Searched in the following locations:
219       - https://dl.google.com/dl/android/maven2/io/grpc/grpc-protobuf/1.21.1/grpc-protobuf-1.21.1.pom
220     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
221     Required by:
222         project : > com.android.tools.build:gradle:7.0.2
223         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
224   Could not find io.grpc:grpc-stub:1.21.1.
225     Searched in the following locations:
226       - https://dl.google.com/dl/android/maven2/io/grpc/grpc-stub/1.21.1/grpc-stub-1.21.1.pom
227     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
228     Required by:
229         project : > com.android.tools.build:gradle:7.0.2
230         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
231   Could not find com.google.crypto.tink:tink:1.3.0-rc2.
232     Searched in the following locations:
233       - https://dl.google.com/dl/android/maven2/com/google/crypto/tink/tink/1.3.0-rc2/tink-1.3.0-rc2.pom
234     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
235     Required by:
236         project : > com.android.tools.build:gradle:7.0.2
237   Could not find com.google.flatbuffers:flatbuffers-java:1.12.0.
238     Searched in the following locations:
239       - https://dl.google.com/dl/android/maven2/com/google/flatbuffers/flatbuffers-java/1.12.0/flatbuffers-java-1.12.0.pom
240     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
241     Required by:
242         project : > com.android.tools.build:gradle:7.0.2
243   Could not find org.tensorflow:tensorflow-lite-metadata:0.1.0-rc2.
244     Searched in the following locations:
245       - https://dl.google.com/dl/android/maven2/org/tensorflow/tensorflow-lite-metadata/0.1.0-rc2/tensorflow-lite-metadata-0.1.0-rc2.pom
246     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
247     Required by:
248         project : > com.android.tools.build:gradle:7.0.2
249   Could not find org.bouncycastle:bcprov-jdk15on:1.56.
250     Searched in the following locations:
251       - https://dl.google.com/dl/android/maven2/org/bouncycastle/bcprov-jdk15on/1.56/bcprov-jdk15on-1.56.pom
252     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
253     Required by:
254         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
255         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
256         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.build:apkzlib:7.0.2
257   Could not find com.google.guava:guava:30.1-jre.
258     Searched in the following locations:
259       - https://dl.google.com/dl/android/maven2/com/google/guava/guava/30.1-jre/guava-30.1-jre.pom
260     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
261     Required by:
262         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
263         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:aaptcompiler:7.0.2
264         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.analytics-library:crash:30.0.2
265         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.analytics-library:shared:30.0.2
266         project : > com.android.tools.build:gradle:7.0.2 > androidx.databinding:databinding-compiler-common:7.0.2
267         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder-test-api:7.0.2
268         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.utp:android-test-plugin-result-listener-gradle-proto:30.0.2
269         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:bundletool:1.6.0
270         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:gradle-api:7.0.2
271         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2 > com.android.tools:common:30.0.2
272         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.analytics-library:tracker:30.0.2
273   Could not find org.jetbrains.kotlin:kotlin-reflect:1.4.32.
274     Searched in the following locations:
275       - https://dl.google.com/dl/android/maven2/org/jetbrains/kotlin/kotlin-reflect/1.4.32/kotlin-reflect-1.4.32.pom
276     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
277     Required by:
278         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
279   Could not find javax.inject:javax.inject:1.
280     Searched in the following locations:
281       - https://dl.google.com/dl/android/maven2/javax/inject/javax.inject/1/javax.inject-1.pom
282     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
283     Required by:
284         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
285         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:bundletool:1.6.0
286         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2
287   Could not find net.sf.kxml:kxml2:2.3.0.
288     Searched in the following locations:
289       - https://dl.google.com/dl/android/maven2/net/sf/kxml/kxml2/2.3.0/kxml2-2.3.0.pom
290     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
291     Required by:
292         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
293         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.ddms:ddmlib:30.0.2
294         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.lint:lint-model:30.0.2
295         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.layoutlib:layoutlib-api:30.0.2
296         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools.build:builder:7.0.2 > com.android.tools.build:manifest-merger:30.0.2
297   Could not find org.jetbrains.intellij.deps:trove4j:1.0.20181211.
298     Searched in the following locations:
299       - https://dl.google.com/dl/android/maven2/org/jetbrains/intellij/deps/trove4j/1.0.20181211/trove4j-1.0.20181211.pom
300     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
301     Required by:
302         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
303   Could not find xerces:xercesImpl:2.12.0.
304     Searched in the following locations:
305       - https://dl.google.com/dl/android/maven2/xerces/xercesImpl/2.12.0/xercesImpl-2.12.0.pom
306     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
307     Required by:
308         project : > com.android.tools.build:gradle:7.0.2 > com.android.tools:sdk-common:30.0.2
309   Could not find org.apache.commons:commons-compress:1.20.
310     Searched in the following locations:
311       - https://dl.google.com/dl/android/maven2/org/apache/commons/commons-compress/1.20/commons-compress-1.20.pom
312     If the artifact you are trying to retrieve can be found in the repository but without metadata in 'Maven POM' format, you need to adjust the 'metadataSources { ... }' of the repository declaration.
313

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41conda create --name py38 python=3.8.0
42conda activate py38
43conda install tensorflow=2.4
44

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41conda create --name py38 python=3.8.0
42conda activate py38
43conda install tensorflow=2.4
44devtools::install_github("rstudio/reticulate")
45library(reticulate)
46reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
47reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
48reticulate::py_available(initialize = TRUE)
49ts <- reticulate::import("tensorflow")
50

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41conda create --name py38 python=3.8.0
42conda activate py38
43conda install tensorflow=2.4
44devtools::install_github("rstudio/reticulate")
45library(reticulate)
46reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
47reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
48reticulate::py_available(initialize = TRUE)
49ts <- reticulate::import("tensorflow")
50Error in py_module_import(module, convert = convert) : 
51  ImportError: Traceback (most recent call last):
52  File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
53    from tensorflow.python._pywrap_tensorflow_internal import *
54  File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
55    module = _import(
56ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
57
58
59Failed to load the native TensorFlow runtime.
60
61See https://www.tensorflow.org/install/errors
62
63for some common reasons and solutions.  Include the entire stack trace
64above this error message when asking for help.
65

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41conda create --name py38 python=3.8.0
42conda activate py38
43conda install tensorflow=2.4
44devtools::install_github("rstudio/reticulate")
45library(reticulate)
46reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
47reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
48reticulate::py_available(initialize = TRUE)
49ts <- reticulate::import("tensorflow")
50Error in py_module_import(module, convert = convert) : 
51  ImportError: Traceback (most recent call last):
52  File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
53    from tensorflow.python._pywrap_tensorflow_internal import *
54  File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
55    module = _import(
56ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
57
58
59Failed to load the native TensorFlow runtime.
60
61See https://www.tensorflow.org/install/errors
62
63for some common reasons and solutions.  Include the entire stack trace
64above this error message when asking for help.
65> strings /lib64/libstdc++.so.6 | grep GLIBC
66
67GLIBCXX_3.4
68GLIBCXX_3.4.1
69GLIBCXX_3.4.2
70GLIBCXX_3.4.3
71GLIBCXX_3.4.4
72GLIBCXX_3.4.5
73GLIBCXX_3.4.6
74GLIBCXX_3.4.7
75GLIBCXX_3.4.8
76GLIBCXX_3.4.9
77GLIBCXX_3.4.10
78GLIBCXX_3.4.11
79GLIBCXX_3.4.12
80GLIBCXX_3.4.13
81GLIBCXX_3.4.14
82GLIBCXX_3.4.15
83GLIBCXX_3.4.16
84GLIBCXX_3.4.17
85GLIBCXX_3.4.18
86GLIBCXX_3.4.19
87GLIBC_2.3
88GLIBC_2.2.5
89GLIBC_2.14
90GLIBC_2.4
91GLIBC_2.3.2
92GLIBCXX_DEBUG_MESSAGE_LENGTH
93

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41conda create --name py38 python=3.8.0
42conda activate py38
43conda install tensorflow=2.4
44devtools::install_github("rstudio/reticulate")
45library(reticulate)
46reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
47reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
48reticulate::py_available(initialize = TRUE)
49ts <- reticulate::import("tensorflow")
50Error in py_module_import(module, convert = convert) : 
51  ImportError: Traceback (most recent call last):
52  File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
53    from tensorflow.python._pywrap_tensorflow_internal import *
54  File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
55    module = _import(
56ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
57
58
59Failed to load the native TensorFlow runtime.
60
61See https://www.tensorflow.org/install/errors
62
63for some common reasons and solutions.  Include the entire stack trace
64above this error message when asking for help.
65> strings /lib64/libstdc++.so.6 | grep GLIBC
66
67GLIBCXX_3.4
68GLIBCXX_3.4.1
69GLIBCXX_3.4.2
70GLIBCXX_3.4.3
71GLIBCXX_3.4.4
72GLIBCXX_3.4.5
73GLIBCXX_3.4.6
74GLIBCXX_3.4.7
75GLIBCXX_3.4.8
76GLIBCXX_3.4.9
77GLIBCXX_3.4.10
78GLIBCXX_3.4.11
79GLIBCXX_3.4.12
80GLIBCXX_3.4.13
81GLIBCXX_3.4.14
82GLIBCXX_3.4.15
83GLIBCXX_3.4.16
84GLIBCXX_3.4.17
85GLIBCXX_3.4.18
86GLIBCXX_3.4.19
87GLIBC_2.3
88GLIBC_2.2.5
89GLIBC_2.14
90GLIBC_2.4
91GLIBC_2.3.2
92GLIBCXX_DEBUG_MESSAGE_LENGTH
93system('export LD_LIBRARY_PATH=/root/.conda/envs/py38/lib/:$LD_LIBRARY_PATH')
94

1devtools::install_github("rstudio/reticulate")
2devtools::install_github("rstudio/keras") # This package also installs tensorflow
3library(reticulate)
4reticulate::install_miniconda()
5reticulate::use_miniconda("r-reticulate")
6library(tensorflow)
7tensorflow::tf_config() **# Crashes at this point**
8
9sessionInfo()
10
11
12R version 3.6.0 (2019-04-26)
13Platform: x86_64-redhat-linux-gnu (64-bit)
14Running under: CentOS Linux 7 (Core)
15
16Matrix products: default
17BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
18
19locale:
20 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
21 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
22 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
23 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
24 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
25[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
26
27attached base packages:
28[1] stats     graphics  grDevices utils     datasets  methods   base     
29
30other attached packages:
31[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 
32
33loaded via a namespace (and not attached):
34 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
35 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
36 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
37[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
38[17] base64enc_0.1-3
39
40
41conda create --name py38 python=3.8.0
42conda activate py38
43conda install tensorflow=2.4
44devtools::install_github("rstudio/reticulate")
45library(reticulate)
46reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
47reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
48reticulate::py_available(initialize = TRUE)
49ts <- reticulate::import("tensorflow")
50Error in py_module_import(module, convert = convert) : 
51  ImportError: Traceback (most recent call last):
52  File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
53    from tensorflow.python._pywrap_tensorflow_internal import *
54  File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
55    module = _import(
56ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)
57
58
59Failed to load the native TensorFlow runtime.
60
61See https://www.tensorflow.org/install/errors
62
63for some common reasons and solutions.  Include the entire stack trace
64above this error message when asking for help.
65> strings /lib64/libstdc++.so.6 | grep GLIBC
66
67GLIBCXX_3.4
68GLIBCXX_3.4.1
69GLIBCXX_3.4.2
70GLIBCXX_3.4.3
71GLIBCXX_3.4.4
72GLIBCXX_3.4.5
73GLIBCXX_3.4.6
74GLIBCXX_3.4.7
75GLIBCXX_3.4.8
76GLIBCXX_3.4.9
77GLIBCXX_3.4.10
78GLIBCXX_3.4.11
79GLIBCXX_3.4.12
80GLIBCXX_3.4.13
81GLIBCXX_3.4.14
82GLIBCXX_3.4.15
83GLIBCXX_3.4.16
84GLIBCXX_3.4.17
85GLIBCXX_3.4.18
86GLIBCXX_3.4.19
87GLIBC_2.3
88GLIBC_2.2.5
89GLIBC_2.14
90GLIBC_2.4
91GLIBC_2.3.2
92GLIBCXX_DEBUG_MESSAGE_LENGTH
93system('export LD_LIBRARY_PATH=/root/.conda/envs/py38/lib/:$LD_LIBRARY_PATH')
94Sys.setenv("LD_LIBRARY_PATH" = "/root/.conda/envs/py38/lib")
95

1input_shape_rgb = (img_height, img_width, 3)
2data_augmentation_rgb = tf.keras.Sequential(
3  [ 
4    layers.RandomFlip("horizontal"),
5    layers.RandomFlip("vertical"),
6    layers.RandomRotation(0.5),
7    layers.RandomZoom(0.5),
8    layers.RandomContrast(0.5),
9    RandomColorDistortion(name='random_contrast_brightness/none'),
10  ]
11)
12

1input_shape_rgb = (img_height, img_width, 3)
2data_augmentation_rgb = tf.keras.Sequential(
3  [ 
4    layers.RandomFlip("horizontal"),
5    layers.RandomFlip("vertical"),
6    layers.RandomRotation(0.5),
7    layers.RandomZoom(0.5),
8    layers.RandomContrast(0.5),
9    RandomColorDistortion(name='random_contrast_brightness/none'),
10  ]
11)
12# Build the model
13input_shape = (img_height, img_width, 3)
14
15model = Sequential([
16  layers.Input(input_shape),
17  data_augmentation_rgb,
18  layers.Rescaling((1./255)),
19
20  layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
21     data_format='channels_last'),
22  layers.MaxPooling2D(),
23  layers.BatchNormalization(),
24
25  layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
26  layers.MaxPooling2D(),
27  layers.BatchNormalization(),
28
29  layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
30  layers.MaxPooling2D(),
31  layers.BatchNormalization(),
32
33  layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
34  layers.MaxPooling2D(),
35  layers.BatchNormalization(),
36
37  layers.Flatten(),
38  layers.Dense(128, activation='relu'), # best 1
39  layers.Dropout(0.1),
40  layers.Dense(128, activation='relu'), # best 1
41  layers.Dropout(0.1),
42  layers.Dense(64, activation='relu'), # best 1
43  layers.Dropout(0.1),
44  layers.Dense(num_classes, activation = 'softmax')
45 ])
46
47 model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=metrics)
48 model.summary()
49

1input_shape_rgb = (img_height, img_width, 3)
2data_augmentation_rgb = tf.keras.Sequential(
3  [ 
4    layers.RandomFlip("horizontal"),
5    layers.RandomFlip("vertical"),
6    layers.RandomRotation(0.5),
7    layers.RandomZoom(0.5),
8    layers.RandomContrast(0.5),
9    RandomColorDistortion(name='random_contrast_brightness/none'),
10  ]
11)
12# Build the model
13input_shape = (img_height, img_width, 3)
14
15model = Sequential([
16  layers.Input(input_shape),
17  data_augmentation_rgb,
18  layers.Rescaling((1./255)),
19
20  layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
21     data_format='channels_last'),
22  layers.MaxPooling2D(),
23  layers.BatchNormalization(),
24
25  layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
26  layers.MaxPooling2D(),
27  layers.BatchNormalization(),
28
29  layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
30  layers.MaxPooling2D(),
31  layers.BatchNormalization(),
32
33  layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
34  layers.MaxPooling2D(),
35  layers.BatchNormalization(),
36
37  layers.Flatten(),
38  layers.Dense(128, activation='relu'), # best 1
39  layers.Dropout(0.1),
40  layers.Dense(128, activation='relu'), # best 1
41  layers.Dropout(0.1),
42  layers.Dense(64, activation='relu'), # best 1
43  layers.Dropout(0.1),
44  layers.Dense(num_classes, activation = 'softmax')
45 ])
46
47 model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=metrics)
48 model.summary()
49model.save("./")
50

1input_shape_rgb = (img_height, img_width, 3)
2data_augmentation_rgb = tf.keras.Sequential(
3  [ 
4    layers.RandomFlip("horizontal"),
5    layers.RandomFlip("vertical"),
6    layers.RandomRotation(0.5),
7    layers.RandomZoom(0.5),
8    layers.RandomContrast(0.5),
9    RandomColorDistortion(name='random_contrast_brightness/none'),
10  ]
11)
12# Build the model
13input_shape = (img_height, img_width, 3)
14
15model = Sequential([
16  layers.Input(input_shape),
17  data_augmentation_rgb,
18  layers.Rescaling((1./255)),
19
20  layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
21     data_format='channels_last'),
22  layers.MaxPooling2D(),
23  layers.BatchNormalization(),
24
25  layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
26  layers.MaxPooling2D(),
27  layers.BatchNormalization(),
28
29  layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
30  layers.MaxPooling2D(),
31  layers.BatchNormalization(),
32
33  layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
34  layers.MaxPooling2D(),
35  layers.BatchNormalization(),
36
37  layers.Flatten(),
38  layers.Dense(128, activation='relu'), # best 1
39  layers.Dropout(0.1),
40  layers.Dense(128, activation='relu'), # best 1
41  layers.Dropout(0.1),
42  layers.Dense(64, activation='relu'), # best 1
43  layers.Dropout(0.1),
44  layers.Dense(num_classes, activation = 'softmax')
45 ])
46
47 model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=metrics)
48 model.summary()
49model.save("./")
50---------------------------------------------------------------------------
51KeyError                                  Traceback (most recent call last)
52<ipython-input-84-87d3f09f8bee> in <module>()
53----> 1 model.save("./")
54
55
56/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in 
57 error_handler(*args, **kwargs)
58 65     except Exception as e:  # pylint: disable=broad-except
59 66       filtered_tb = _process_traceback_frames(e.__traceback__)
60 ---> 67       raise e.with_traceback(filtered_tb) from None
61 68     finally:
62 69       del filtered_tb
63
64 /usr/local/lib/python3.7/dist- 
65 packages/tensorflow/python/saved_model/function_serialization.py in 
66 serialize_concrete_function(concrete_function, node_ids, coder)
67 66   except KeyError:
68 67     raise KeyError(
69 ---> 68         f"Failed to add concrete function '{concrete_function.name}' to 
70 object-"
71 69         f"based SavedModel as it captures tensor {capture!r} which is 
72 unsupported"
73 70         " or not reachable from root. "
74
75 KeyError: "Failed to add concrete function 
76 'b'__inference_sequential_46_layer_call_fn_662953'' to object-based SavedModel as it 
77 captures tensor <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>> which 
78 is unsupported or not reachable from root. One reason could be that a stateful 
79 object or a variable that the function depends on is not assigned to an attribute of 
80 the serialized trackable object (see SaveTest.test_captures_unreachable_variable)."
81

1import keras
2from keras.datasets import cifar10
3from keras.utils import to_categorical
4from keras.models import Sequential
5from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPooling2D
6from keras.layers.normalization import BatchNormalization
7
8(X_train, y_train), (X_test, y_test) = cifar10.load_data()
9
10#reshape data to fit model
11X_train = X_train.reshape(50000,32,32,3)
12X_test = X_test.reshape(10000,32,32,3)
13
14y_train = to_categorical(y_train)
15y_test = to_categorical(y_test)
16
17
18# Building the model 
19
20#1st Convolutional Layer
21model.add(Conv2D(filters=64, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding='same'))
22model.add(BatchNormalization())
23model.add(Activation('relu'))
24model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
25
26#2nd Convolutional Layer
27model.add(Conv2D(filters=224, kernel_size=(5, 5), strides=(1,1), padding='same'))
28model.add(BatchNormalization())
29model.add(Activation('relu'))
30model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
31
32#3rd Convolutional Layer
33model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
34model.add(BatchNormalization())
35model.add(Activation('relu'))
36
37#4th Convolutional Layer
38model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
39model.add(BatchNormalization())
40model.add(Activation('relu'))
41
42#5th Convolutional Layer
43model.add(Conv2D(filters=160, kernel_size=(3,3), strides=(1,1), padding='same'))
44model.add(BatchNormalization())
45model.add(Activation('relu'))
46model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
47
48model.add(Flatten())
49
50# 1st Fully Connected Layer
51model.add(Dense(4096, input_shape=(32,32,3,)))
52model.add(BatchNormalization())
53model.add(Activation('relu'))
54# Add Dropout to prevent overfitting
55model.add(Dropout(0.4))
56
57#2nd Fully Connected Layer
58model.add(Dense(4096))
59model.add(BatchNormalization())
60model.add(Activation('relu'))
61#Add Dropout
62model.add(Dropout(0.4))
63
64#3rd Fully Connected Layer
65model.add(Dense(1000))
66model.add(BatchNormalization())
67model.add(Activation('relu'))
68#Add Dropout
69model.add(Dropout(0.4))
70
71#Output Layer
72model.add(Dense(10))
73model.add(BatchNormalization())
74model.add(Activation('softmax'))
75
76
77#compile model using accuracy to measure model performance
78opt = keras.optimizers.Adam(learning_rate = 0.0001)
79model.compile(optimizer=opt, loss='categorical_crossentropy', 
80              metrics=['accuracy'])
81
82
83#train the model
84model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30)
85

1import keras
2from keras.datasets import cifar10
3from keras.utils import to_categorical
4from keras.models import Sequential
5from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPooling2D
6from keras.layers.normalization import BatchNormalization
7
8(X_train, y_train), (X_test, y_test) = cifar10.load_data()
9
10#reshape data to fit model
11X_train = X_train.reshape(50000,32,32,3)
12X_test = X_test.reshape(10000,32,32,3)
13
14y_train = to_categorical(y_train)
15y_test = to_categorical(y_test)
16
17
18# Building the model 
19
20#1st Convolutional Layer
21model.add(Conv2D(filters=64, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding='same'))
22model.add(BatchNormalization())
23model.add(Activation('relu'))
24model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
25
26#2nd Convolutional Layer
27model.add(Conv2D(filters=224, kernel_size=(5, 5), strides=(1,1), padding='same'))
28model.add(BatchNormalization())
29model.add(Activation('relu'))
30model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
31
32#3rd Convolutional Layer
33model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
34model.add(BatchNormalization())
35model.add(Activation('relu'))
36
37#4th Convolutional Layer
38model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
39model.add(BatchNormalization())
40model.add(Activation('relu'))
41
42#5th Convolutional Layer
43model.add(Conv2D(filters=160, kernel_size=(3,3), strides=(1,1), padding='same'))
44model.add(BatchNormalization())
45model.add(Activation('relu'))
46model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
47
48model.add(Flatten())
49
50# 1st Fully Connected Layer
51model.add(Dense(4096, input_shape=(32,32,3,)))
52model.add(BatchNormalization())
53model.add(Activation('relu'))
54# Add Dropout to prevent overfitting
55model.add(Dropout(0.4))
56
57#2nd Fully Connected Layer
58model.add(Dense(4096))
59model.add(BatchNormalization())
60model.add(Activation('relu'))
61#Add Dropout
62model.add(Dropout(0.4))
63
64#3rd Fully Connected Layer
65model.add(Dense(1000))
66model.add(BatchNormalization())
67model.add(Activation('relu'))
68#Add Dropout
69model.add(Dropout(0.4))
70
71#Output Layer
72model.add(Dense(10))
73model.add(BatchNormalization())
74model.add(Activation('softmax'))
75
76
77#compile model using accuracy to measure model performance
78opt = keras.optimizers.Adam(learning_rate = 0.0001)
79model.compile(optimizer=opt, loss='categorical_crossentropy', 
80              metrics=['accuracy'])
81
82
83#train the model
84model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30)
85import keras
86import scipy
87import numpy as np
88import matplotlib.pyplot as plt
89from keras.utils import to_categorical
90from scipy import io
91
92mydict = scipy.io.loadmat('Indian_pines_corrected.mat')
93dataset = np.array(mydict.get('indian_pines_corrected'))
94
95
96#This is creating the split between x_train and x_test from the original dataset 
97# x_train after this code runs will have a shape of (121, 145, 200) 
98# x_test after this code runs will have a shape of (24, 145, 200)
99x_train = np.zeros((121,145,200), dtype=np.int)
100x_test = np.zeros((24,145,200), dtype=np.int)    
101
102xtemp = np.array_split(dataset, [121])
103x_train = np.array(xtemp[0])
104x_test = np.array(xtemp[1])
105
106# x_train will have a shape of (17545, 200) 
107# x_test will have a shape of (3480, 200)
108x_train = x_train.reshape(-1, x_train.shape[-1])
109x_test = x_test.reshape(-1, x_test.shape[-1])
110

1import keras
2from keras.datasets import cifar10
3from keras.utils import to_categorical
4from keras.models import Sequential
5from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPooling2D
6from keras.layers.normalization import BatchNormalization
7
8(X_train, y_train), (X_test, y_test) = cifar10.load_data()
9
10#reshape data to fit model
11X_train = X_train.reshape(50000,32,32,3)
12X_test = X_test.reshape(10000,32,32,3)
13
14y_train = to_categorical(y_train)
15y_test = to_categorical(y_test)
16
17
18# Building the model 
19
20#1st Convolutional Layer
21model.add(Conv2D(filters=64, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding='same'))
22model.add(BatchNormalization())
23model.add(Activation('relu'))
24model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
25
26#2nd Convolutional Layer
27model.add(Conv2D(filters=224, kernel_size=(5, 5), strides=(1,1), padding='same'))
28model.add(BatchNormalization())
29model.add(Activation('relu'))
30model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
31
32#3rd Convolutional Layer
33model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
34model.add(BatchNormalization())
35model.add(Activation('relu'))
36
37#4th Convolutional Layer
38model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
39model.add(BatchNormalization())
40model.add(Activation('relu'))
41
42#5th Convolutional Layer
43model.add(Conv2D(filters=160, kernel_size=(3,3), strides=(1,1), padding='same'))
44model.add(BatchNormalization())
45model.add(Activation('relu'))
46model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))
47
48model.add(Flatten())
49
50# 1st Fully Connected Layer
51model.add(Dense(4096, input_shape=(32,32,3,)))
52model.add(BatchNormalization())
53model.add(Activation('relu'))
54# Add Dropout to prevent overfitting
55model.add(Dropout(0.4))
56
57#2nd Fully Connected Layer
58model.add(Dense(4096))
59model.add(BatchNormalization())
60model.add(Activation('relu'))
61#Add Dropout
62model.add(Dropout(0.4))
63
64#3rd Fully Connected Layer
65model.add(Dense(1000))
66model.add(BatchNormalization())
67model.add(Activation('relu'))
68#Add Dropout
69model.add(Dropout(0.4))
70
71#Output Layer
72model.add(Dense(10))
73model.add(BatchNormalization())
74model.add(Activation('softmax'))
75
76
77#compile model using accuracy to measure model performance
78opt = keras.optimizers.Adam(learning_rate = 0.0001)
79model.compile(optimizer=opt, loss='categorical_crossentropy', 
80              metrics=['accuracy'])
81
82
83#train the model
84model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30)
85import keras
86import scipy
87import numpy as np
88import matplotlib.pyplot as plt
89from keras.utils import to_categorical
90from scipy import io
91
92mydict = scipy.io.loadmat('Indian_pines_corrected.mat')
93dataset = np.array(mydict.get('indian_pines_corrected'))
94
95
96#This is creating the split between x_train and x_test from the original dataset 
97# x_train after this code runs will have a shape of (121, 145, 200) 
98# x_test after this code runs will have a shape of (24, 145, 200)
99x_train = np.zeros((121,145,200), dtype=np.int)
100x_test = np.zeros((24,145,200), dtype=np.int)    
101
102xtemp = np.array_split(dataset, [121])
103x_train = np.array(xtemp[0])
104x_test = np.array(xtemp[1])
105
106# x_train will have a shape of (17545, 200) 
107# x_test will have a shape of (3480, 200)
108x_train = x_train.reshape(-1, x_train.shape[-1])
109x_test = x_test.reshape(-1, x_test.shape[-1])
110truthDataset = scipy.io.loadmat('Indian_pines_gt.mat')
111gTruth = truthDataset.get('indian_pines_gt')
112
113#This is creating the split between Y_train and Y_test from the original dataset 
114# Y_train after this code runs will have a shape of (121, 145) 
115# Y_test after this code runs will have a shape of (24, 145)
116
117Y_train = np.zeros((121,145), dtype=np.int)
118Y_test = np.zeros((24,145), dtype=np.int)    
119
120ytemp = np.array_split(gTruth, [121])
121Y_train = np.array(ytemp[0])
122Y_test = np.array(ytemp[1])
123
124# Y_train will have a shape of (17545) 
125# Y_test will have a shape of (3480)
126Y_train = Y_train.reshape(-1)
127Y_test = Y_test.reshape(-1)
128
129
130#17 binary categories ranging from 0-16
131
132#Y_train one-hot encode target column
133Y_train = to_categorical(Y_train)
134
135#Y_test one-hot encode target column
136Y_test = to_categorical(Y_test, num_classes = 17)
137

1from keras.models import Sequential
2from keras.layers.normalization import BatchNormalization
3

1from keras.models import Sequential
2from keras.layers.normalization import BatchNormalization
32021-10-06 22:27:14.064885: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
42021-10-06 22:27:14.064974: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
5Traceback (most recent call last):
6  File "C:\Data\breast-cancer-classification\train_model.py", line 10, in <module>
7    from cancernet.cancernet import CancerNet
8  File "C:\Data\breast-cancer-classification\cancernet\cancernet.py", line 2, in <module>
9    from keras.layers.normalization import BatchNormalization
10ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\Users\Catalin\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\layers\normalization\__init__.py)
11

1from keras.models import Sequential
2from keras.layers.normalization import BatchNormalization
32021-10-06 22:27:14.064885: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
42021-10-06 22:27:14.064974: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
5Traceback (most recent call last):
6  File "C:\Data\breast-cancer-classification\train_model.py", line 10, in <module>
7    from cancernet.cancernet import CancerNet
8  File "C:\Data\breast-cancer-classification\cancernet\cancernet.py", line 2, in <module>
9    from keras.layers.normalization import BatchNormalization
10ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\Users\Catalin\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\layers\normalization\__init__.py)
11pip install numpy opencv-python pillow tensorflow keras imutils scikit-learn matplotlib
12

1def action(y,x):
2    #spacing between points.
3    h = (x[-1] - x[0]) / (int(x.shape[0]) - 1)
4    
5    #fourth derivative 
6    dy4 = (y[4:] - 4*y[3:-1] + 6*y[2:-2] - 4*y[1:-3] + y[:-4])/(h*h*h*h)
7
8    return dy4
9
10x = tf.linspace(0.0, 30, 1000)
11y = tf.tanh(x)
12dy4 = action(y,x)
13
14sess = tf.compat.v1.Session()
15plt.plot(sess.run(dy4))
16

1def action(y,x):
2    #spacing between points.
3    h = (x[-1] - x[0]) / (int(x.shape[0]) - 1)
4    
5    #fourth derivative 
6    dy4 = (y[4:] - 4*y[3:-1] + 6*y[2:-2] - 4*y[1:-3] + y[:-4])/(h*h*h*h)
7
8    return dy4
9
10x = tf.linspace(0.0, 30, 1000)
11y = tf.tanh(x)
12dy4 = action(y,x)
13
14sess = tf.compat.v1.Session()
15plt.plot(sess.run(dy4))
16def fourth_deriv(y, x):
17    h = (x[-1] - x[0]) / (int(x.shape[0]) - 1)
18    dy = (y[4:] - 4*y[3:-1] + 6*y[2:-2] - 4*y[1:-3] + y[:-4])/(h*h*h*h)
19    return dy
20
21x = np.linspace(0.0, 30, 1000)
22test = fourth_deriv(np.tanh(x), x)
23plt.plot(test)
24

1@pytest.mark.usefixtures("maybe_run_functions_eagerly")
2def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
3    model, speech_features_config = speech_model
4    speech_features_config: SpeechFeaturesConfig
5    channels = 3 if speech_features_config.add_delta_deltas else 1
6    num_mel_bins = speech_features_config.num_mel_bins
7    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
8    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
9    inputs = enc_inputs, dec_inputs
10    model(inputs)
11
12    # Throws KeyError:
13    # graph = tf.compat.v1.get_default_graph()
14    # tensor = graph.get_tensor_by_name("77040:0")
15
16    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
17    try:
18        model.save(directory)
19    finally:
20        shutil.rmtree(directory)
21

1@pytest.mark.usefixtures("maybe_run_functions_eagerly")
2def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
3    model, speech_features_config = speech_model
4    speech_features_config: SpeechFeaturesConfig
5    channels = 3 if speech_features_config.add_delta_deltas else 1
6    num_mel_bins = speech_features_config.num_mel_bins
7    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
8    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
9    inputs = enc_inputs, dec_inputs
10    model(inputs)
11
12    # Throws KeyError:
13    # graph = tf.compat.v1.get_default_graph()
14    # tensor = graph.get_tensor_by_name("77040:0")
15
16    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
17    try:
18        model.save(directory)
19    finally:
20        shutil.rmtree(directory)
21E         AssertionError: Tried to export a function which references untracked resource Tensor("77040:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
22E         
23E         Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
24E         <tf.Variable 'transformer_transducer/transducer_encoder/inputs_embedding/convolution_stack/conv2d/kernel:0' shape=(3, 3, 3, 32) dtype=float32>
25

1@pytest.mark.usefixtures("maybe_run_functions_eagerly")
2def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
3    model, speech_features_config = speech_model
4    speech_features_config: SpeechFeaturesConfig
5    channels = 3 if speech_features_config.add_delta_deltas else 1
6    num_mel_bins = speech_features_config.num_mel_bins
7    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
8    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
9    inputs = enc_inputs, dec_inputs
10    model(inputs)
11
12    # Throws KeyError:
13    # graph = tf.compat.v1.get_default_graph()
14    # tensor = graph.get_tensor_by_name("77040:0")
15
16    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
17    try:
18        model.save(directory)
19    finally:
20        shutil.rmtree(directory)
21E         AssertionError: Tried to export a function which references untracked resource Tensor("77040:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
22E         
23E         Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
24E         <tf.Variable 'transformer_transducer/transducer_encoder/inputs_embedding/convolution_stack/conv2d/kernel:0' shape=(3, 3, 3, 32) dtype=float32>
25model(batch)  # Build the model
26
27tensor_name = "77040"
28
29var_names = [var.name for var in model.trainable_weights]
30weights = list(filter(lambda var: tensor_name in var, var_names))
31
32var_names = [var.name for var in model.trainable_variables]
33variables = list(filter(lambda var: tensor_name in var, var_names))
34
35print(weights)
36print(variables)
37

1@pytest.mark.usefixtures("maybe_run_functions_eagerly")
2def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
3    model, speech_features_config = speech_model
4    speech_features_config: SpeechFeaturesConfig
5    channels = 3 if speech_features_config.add_delta_deltas else 1
6    num_mel_bins = speech_features_config.num_mel_bins
7    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
8    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
9    inputs = enc_inputs, dec_inputs
10    model(inputs)
11
12    # Throws KeyError:
13    # graph = tf.compat.v1.get_default_graph()
14    # tensor = graph.get_tensor_by_name("77040:0")
15
16    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
17    try:
18        model.save(directory)
19    finally:
20        shutil.rmtree(directory)
21E         AssertionError: Tried to export a function which references untracked resource Tensor("77040:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
22E         
23E         Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
24E         <tf.Variable 'transformer_transducer/transducer_encoder/inputs_embedding/convolution_stack/conv2d/kernel:0' shape=(3, 3, 3, 32) dtype=float32>
25model(batch)  # Build the model
26
27tensor_name = "77040"
28
29var_names = [var.name for var in model.trainable_weights]
30weights = list(filter(lambda var: tensor_name in var, var_names))
31
32var_names = [var.name for var in model.trainable_variables]
33variables = list(filter(lambda var: tensor_name in var, var_names))
34
35print(weights)
36print(variables)
37class InputsEmbedding(layers.Layer, TimeReduction):
38    def __init__(self, config: InputsEmbeddingConfig, **kwargs):
39        super().__init__(**kwargs)
40
41        if config.transformations is None or not len(config.transformations):
42            raise RuntimeError("No transformations provided.")
43
44        self.config = config
45
46        self.transformations = list()
47        for transformation in self.config.transformations:
48            layer_name, layer_params = list(transformation.items())[0]
49            layer = _get_layer(layer_name, layer_params)
50            self.transformations.append(layer)
51
52        self.init_time_reduction_layer()
53
54    def get_config(self):
55        return self.config.dict()
56
57
58def _get_layer(name: str, params: dict) -> layers.Layer:
59    if name == "conv2d_stack":
60        return ConvolutionStack(**params)
61    elif name == "stack_frames":
62        return StackFrames(**params)
63    else:
64        raise RuntimeError(f"Unsupported or unknown time-reduction layer {name}")
65

1@pytest.mark.usefixtures("maybe_run_functions_eagerly")
2def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
3    model, speech_features_config = speech_model
4    speech_features_config: SpeechFeaturesConfig
5    channels = 3 if speech_features_config.add_delta_deltas else 1
6    num_mel_bins = speech_features_config.num_mel_bins
7    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
8    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
9    inputs = enc_inputs, dec_inputs
10    model(inputs)
11
12    # Throws KeyError:
13    # graph = tf.compat.v1.get_default_graph()
14    # tensor = graph.get_tensor_by_name("77040:0")
15
16    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
17    try:
18        model.save(directory)
19    finally:
20        shutil.rmtree(directory)
21E         AssertionError: Tried to export a function which references untracked resource Tensor("77040:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
22E         
23E         Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
24E         <tf.Variable 'transformer_transducer/transducer_encoder/inputs_embedding/convolution_stack/conv2d/kernel:0' shape=(3, 3, 3, 32) dtype=float32>
25model(batch)  # Build the model
26
27tensor_name = "77040"
28
29var_names = [var.name for var in model.trainable_weights]
30weights = list(filter(lambda var: tensor_name in var, var_names))
31
32var_names = [var.name for var in model.trainable_variables]
33variables = list(filter(lambda var: tensor_name in var, var_names))
34
35print(weights)
36print(variables)
37class InputsEmbedding(layers.Layer, TimeReduction):
38    def __init__(self, config: InputsEmbeddingConfig, **kwargs):
39        super().__init__(**kwargs)
40
41        if config.transformations is None or not len(config.transformations):
42            raise RuntimeError("No transformations provided.")
43
44        self.config = config
45
46        self.transformations = list()
47        for transformation in self.config.transformations:
48            layer_name, layer_params = list(transformation.items())[0]
49            layer = _get_layer(layer_name, layer_params)
50            self.transformations.append(layer)
51
52        self.init_time_reduction_layer()
53
54    def get_config(self):
55        return self.config.dict()
56
57
58def _get_layer(name: str, params: dict) -> layers.Layer:
59    if name == "conv2d_stack":
60        return ConvolutionStack(**params)
61    elif name == "stack_frames":
62        return StackFrames(**params)
63    else:
64        raise RuntimeError(f"Unsupported or unknown time-reduction layer {name}")
65@pytest.mark.usefixtures("maybe_run_functions_eagerly")
66def test_inputs_embedding_save_model():
67    convolutions = [
68        "filters=2, kernel_size=(3, 3), strides=(2, 1)",
69        "filters=4, kernel_size=(3, 3), strides=(2, 1)",
70        "filters=8, kernel_size=(3, 4), strides=(1, 1)",
71    ]
72
73    config = InputsEmbeddingConfig()
74    config.transformations = [dict(conv2d_stack=dict(convolutions=convolutions)), dict(stack_frames=dict(n=2))]
75
76    num_features = 8
77    num_channels = 3
78
79    inputs = layers.Input(shape=(None, num_features, num_channels))
80    x = inputs
81    x, _ = InputsEmbedding(config)(x)
82    model = keras.Model(inputs=inputs, outputs=x)
83    model.build(input_shape=(1, 20, num_features, num_channels))
84
85    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
86    try:
87        model.save(directory)
88    finally:
89        shutil.rmtree(directory)
90

1@pytest.mark.usefixtures("maybe_run_functions_eagerly")
2def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
3    model, speech_features_config = speech_model
4    speech_features_config: SpeechFeaturesConfig
5    channels = 3 if speech_features_config.add_delta_deltas else 1
6    num_mel_bins = speech_features_config.num_mel_bins
7    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
8    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
9    inputs = enc_inputs, dec_inputs
10    model(inputs)
11
12    # Throws KeyError:
13    # graph = tf.compat.v1.get_default_graph()
14    # tensor = graph.get_tensor_by_name("77040:0")
15
16    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
17    try:
18        model.save(directory)
19    finally:
20        shutil.rmtree(directory)
21E         AssertionError: Tried to export a function which references untracked resource Tensor("77040:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
22E         
23E         Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
24E         <tf.Variable 'transformer_transducer/transducer_encoder/inputs_embedding/convolution_stack/conv2d/kernel:0' shape=(3, 3, 3, 32) dtype=float32>
25model(batch)  # Build the model
26
27tensor_name = "77040"
28
29var_names = [var.name for var in model.trainable_weights]
30weights = list(filter(lambda var: tensor_name in var, var_names))
31
32var_names = [var.name for var in model.trainable_variables]
33variables = list(filter(lambda var: tensor_name in var, var_names))
34
35print(weights)
36print(variables)
37class InputsEmbedding(layers.Layer, TimeReduction):
38    def __init__(self, config: InputsEmbeddingConfig, **kwargs):
39        super().__init__(**kwargs)
40
41        if config.transformations is None or not len(config.transformations):
42            raise RuntimeError("No transformations provided.")
43
44        self.config = config
45
46        self.transformations = list()
47        for transformation in self.config.transformations:
48            layer_name, layer_params = list(transformation.items())[0]
49            layer = _get_layer(layer_name, layer_params)
50            self.transformations.append(layer)
51
52        self.init_time_reduction_layer()
53
54    def get_config(self):
55        return self.config.dict()
56
57
58def _get_layer(name: str, params: dict) -> layers.Layer:
59    if name == "conv2d_stack":
60        return ConvolutionStack(**params)
61    elif name == "stack_frames":
62        return StackFrames(**params)
63    else:
64        raise RuntimeError(f"Unsupported or unknown time-reduction layer {name}")
65@pytest.mark.usefixtures("maybe_run_functions_eagerly")
66def test_inputs_embedding_save_model():
67    convolutions = [
68        "filters=2, kernel_size=(3, 3), strides=(2, 1)",
69        "filters=4, kernel_size=(3, 3), strides=(2, 1)",
70        "filters=8, kernel_size=(3, 4), strides=(1, 1)",
71    ]
72
73    config = InputsEmbeddingConfig()
74    config.transformations = [dict(conv2d_stack=dict(convolutions=convolutions)), dict(stack_frames=dict(n=2))]
75
76    num_features = 8
77    num_channels = 3
78
79    inputs = layers.Input(shape=(None, num_features, num_channels))
80    x = inputs
81    x, _ = InputsEmbedding(config)(x)
82    model = keras.Model(inputs=inputs, outputs=x)
83    model.build(input_shape=(1, 20, num_features, num_channels))
84
85    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
86    try:
87        model.save(directory)
88    finally:
89        shutil.rmtree(directory)
90from typing import List
91
92import tensorflow as tf
93from tensorflow.keras import layers
94from tensorflow.python.keras.layers import convolutional
95
96from speech.lab.layers import InputsRequirements
97from speech.lab.models import conv_util, models_util
98
99
100class ConvolutionStack(layers.Layer):
101    def __init__(
102        self,
103        convolutions: List[str],
104        kernel_regularizer: dict = None,
105        bias_regularizer: dict = None,
106        **kwargs
107    ):
108        super().__init__(**kwargs)
109        self.config = dict(
110            convolutions=convolutions,
111            kernel_regularizer=kernel_regularizer,
112            bias_regularizer=bias_regularizer
113        )
114        self.conv_stack_config = [eval(f"dict({convolution})") for convolution in convolutions]
115        self.conv_blocks = list()
116
117        if kernel_regularizer is not None:
118            kernel_regularizer = models_util.maybe_to_regularizer(kernel_regularizer)
119        if bias_regularizer is not None:
120            bias_regularizer = models_util.maybe_to_regularizer(bias_regularizer)
121
122        for block_config in self.conv_stack_config:
123            block = _new_convolution_block(
124                **block_config,
125                kernel_regularizer=kernel_regularizer,
126                bias_regularizer=bias_regularizer,
127            )
128            self.conv_blocks.append(block)
129
130        self.drop_dim2 = layers.Lambda(tf.squeeze, arguments=dict(axis=-2))
131        self.expand_last = layers.Lambda(tf.expand_dims, arguments=dict(axis=-1))
132
133    @property
134    def inputs_requirements(self) -> InputsRequirements:
135        requirements, frame_look_back = conv_util.get_conv2d_stack_requirements(self.conv_stack_config)
136        first = requirements[0]
137        t_min, f_size = first["min_size"]
138        t_grow, f_grow = first["grow_size"]
139        return InputsRequirements(
140            frame_look_back=frame_look_back,
141            t_min=t_min,
142            t_grow=t_grow,
143            f_min=f_size,
144            f_grow=f_grow,
145        )
146
147    def call(self, inputs, training=None, mask=None, **kwargs):
148        """
149        :param inputs:
150            Tensor taking the form [batch, time, freq, channel]
151        :param training:
152        :param mask:
153        :param kwargs:
154        :return:
155            Tensor taking the form [batch, time, freq, 1]
156        """
157
158        if training:
159            t_min = self.inputs_requirements.t_min
160            t_grow = self.inputs_requirements.t_grow
161            pad = conv_util.get_padding_for_loss(tf.shape(inputs)[1], t_min=t_min, t_grow=t_grow)
162            inputs = tf.pad(inputs, ((0, 0), (0, pad), (0, 0), (0, 0)))
163
164            if mask is not None:
165                mask = tf.pad(mask, ((0, 0), (0, pad)))
166
167        f_min = self.inputs_requirements.f_min
168        f_grow = self.inputs_requirements.f_grow
169        assert (inputs.shape[2] - f_min) % f_grow == 0, (
170            f'Inputs dimension "freq" ' f"expected to be {f_min} + n * {f_grow}  but got {inputs.shape[2]} instead."
171        )
172
173        x = inputs
174        for block in self.conv_blocks:
175
176            for layer in block:
177
178                if mask is not None and isinstance(layer, convolutional.Conv):
179                    st, _ = layer.strides
180                    kt = tf.maximum(layer.kernel_size[0] - 1, 1)
181                    mask = mask[:, :-kt][:, ::st]
182                    mask = tf.pad(mask, ((0, 0), (0, tf.maximum(2 - layer.kernel_size[0], 0))))
183
184                x = layer(x, training=training)
185
186        return self.expand_last(self.drop_dim2(x)), mask
187
188    def get_config(self):
189        return self.config
190
191
192def _new_convolution_block(
193    filters: int,
194    kernel_size: tuple,
195    strides: tuple,
196    use_bias: bool = False,
197    use_norm: bool = True,
198    kernel_regularizer=None,
199    bias_regularizer=None,
200    activation=None,
201):
202    assert strides[0] % 2 == 0 or strides[0] == 1, "Strides on the time axis must be divisible by 2 or be exactly 1."
203
204    if activation is not None:
205        activation_layer = layers.Activation(activation)
206    else:
207        activation_layer = layers.Lambda(lambda x: x)
208
209    if use_norm:
210        norm_layer = layers.LayerNormalization()
211    else:
212        norm_layer = layers.Lambda(lambda x: x)
213
214    return (
215        layers.Conv2D(
216            filters=filters,
217            kernel_size=kernel_size,
218            strides=strides,
219            use_bias=use_bias,
220            kernel_regularizer=kernel_regularizer,
221            bias_regularizer=bias_regularizer,
222        ),
223        norm_layer,
224        activation_layer,
225    )
226

1Import tensorflow as tf
2tf.config.list_physical_devices()
3

1Import tensorflow as tf
2tf.config.list_physical_devices()
3 E tensorflow/stream_executor/cuda/cuda_driver.cc:355] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
4