tutorials 09

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=63288

import sys; print('Python %s on %s' % (sys.version, sys.platform))

sys.path.extend(['O:\\PycharmProjects\\catdogtf2.2', 'O:/PycharmProjects/catdogtf2.2'])

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)]

Type 'copyright', 'credits' or 'license' for more information

IPython 7.17.0 -- An enhanced Interactive Python. Type '?' for help.

PyDev console: using IPython 7.17.0

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] on win32

runfile('O:/PycharmProjects/catdogtf2.2/009.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 23:38:38.375911: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

Downloading data from https://storage.googleapis.com/applied-dl/heart.csv

16384/13273 [=====================================] - 0s 0us/step

2020-08-11 23:38:41.529035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 23:38:41.572391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:

pciBusID: 0000:09:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5

coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s

2020-08-11 23:38:41.572897: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:38:41.585503: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:38:41.592012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:38:41.595184: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:38:41.602528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:38:41.607053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:38:41.619809: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:38:41.620202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:38:41.620769: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

2020-08-11 23:38:41.630669: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2417f28da80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:38:41.631101: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 23:38:41.631514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:

pciBusID: 0000:09:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5

coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s

2020-08-11 23:38:41.631942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:38:41.632082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:38:41.632363: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:38:41.632610: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:38:41.632981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:38:41.633288: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:38:41.633583: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:38:41.633919: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:38:42.309719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 23:38:42.309959: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 23:38:42.310107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 23:38:42.310492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5)

2020-08-11 23:38:42.313704: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2412eb83730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:38:42.314096: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Features: [ 63.    1.    1.  145.  233.    1.    2.  150.    0.    2.3   3.    0.

   2. ], Target: 0

Features: [ 67.    1.    4.  160.  286.    0.    2.  108.    1.    1.5   2.    3.

   3. ], Target: 1

Features: [ 67.    1.    4.  120.  229.    0.    2.  129.    1.    2.6   2.    2.

   4. ], Target: 0

Features: [ 37.    1.    3.  130.  250.    0.    0.  187.    0.    3.5   3.    0.

   3. ], Target: 0

Features: [ 41.    0.    2.  130.  204.    0.    2.  172.    0.    1.4   1.    0.

   3. ], Target: 0

Epoch 1/15

WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

2020-08-11 23:38:43.103814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

303/303 [==============================] - 1s 2ms/step - loss: 1.0260 - accuracy: 0.6832

Epoch 2/15

303/303 [==============================] - 1s 2ms/step - loss: 0.7020 - accuracy: 0.7558

Epoch 3/15

303/303 [==============================] - 1s 3ms/step - loss: 0.6972 - accuracy: 0.7294

Epoch 4/15

303/303 [==============================] - 1s 3ms/step - loss: 0.6353 - accuracy: 0.7294

Epoch 5/15

303/303 [==============================] - 1s 3ms/step - loss: 0.6456 - accuracy: 0.7492

Epoch 6/15

303/303 [==============================] - 1s 2ms/step - loss: 0.6248 - accuracy: 0.7492

Epoch 7/15

303/303 [==============================] - 1s 2ms/step - loss: 0.4927 - accuracy: 0.7855

Epoch 8/15

303/303 [==============================] - 1s 3ms/step - loss: 0.5099 - accuracy: 0.7756

Epoch 9/15

303/303 [==============================] - 1s 2ms/step - loss: 0.5669 - accuracy: 0.7492

Epoch 10/15

303/303 [==============================] - 1s 2ms/step - loss: 0.5558 - accuracy: 0.7888

Epoch 11/15

303/303 [==============================] - 1s 2ms/step - loss: 0.5408 - accuracy: 0.7624

Epoch 12/15

303/303 [==============================] - 1s 2ms/step - loss: 0.4900 - accuracy: 0.7987

Epoch 13/15

303/303 [==============================] - 1s 2ms/step - loss: 0.4866 - accuracy: 0.8053

Epoch 14/15

303/303 [==============================] - 1s 3ms/step - loss: 0.4382 - accuracy: 0.7855

Epoch 15/15

303/303 [==============================] - 1s 3ms/step - loss: 0.4960 - accuracy: 0.7657

({'age': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 52, 57])>, 'sex': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1])>, 'cp': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3])>, 'trestbps': <tf.Tensor: shape=(16,), dtype=int32, numpy=

array([145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 140, 130,

       120, 172, 150])>, 'chol': <tf.Tensor: shape=(16,), dtype=int32, numpy=

array([233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 294, 256,

       263, 199, 168])>, 'fbs': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0])>, 'restecg': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0])>, 'thalach': <tf.Tensor: shape=(16,), dtype=int32, numpy=

array([150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 153, 142,

       173, 162, 174])>, 'exang': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0])>, 'oldpeak': <tf.Tensor: shape=(16,), dtype=float32, numpy=

array([2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.3, 0.6,

       0. , 0.5, 1.6], dtype=float32)>, 'slope': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1])>, 'ca': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0])>, 'thal': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([2, 3, 4, 3, 3, 3, 3, 3, 4, 4, 2, 3, 2, 4, 4, 3])>}, <tf.Tensor: shape=(16,), dtype=int64, numpy=array([0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)>)

Epoch 1/15

19/19 [==============================] - 0s 3ms/step - loss: 167.4701 - accuracy: 0.2739

Epoch 2/15

19/19 [==============================] - 0s 3ms/step - loss: 139.3064 - accuracy: 0.2739

Epoch 3/15

19/19 [==============================] - 0s 3ms/step - loss: 111.9945 - accuracy: 0.2739

Epoch 4/15

19/19 [==============================] - 0s 4ms/step - loss: 84.2170 - accuracy: 0.2739

Epoch 5/15

19/19 [==============================] - 0s 3ms/step - loss: 53.5864 - accuracy: 0.2739

Epoch 6/15

19/19 [==============================] - 0s 3ms/step - loss: 19.6813 - accuracy: 0.3069

Epoch 7/15

19/19 [==============================] - 0s 3ms/step - loss: 3.5970 - accuracy: 0.6766

Epoch 8/15

19/19 [==============================] - 0s 3ms/step - loss: 3.0850 - accuracy: 0.7030

Epoch 9/15

19/19 [==============================] - 0s 3ms/step - loss: 2.6416 - accuracy: 0.6403

Epoch 10/15

19/19 [==============================] - 0s 3ms/step - loss: 2.4151 - accuracy: 0.6766

Epoch 11/15

19/19 [==============================] - 0s 3ms/step - loss: 2.2261 - accuracy: 0.6766

Epoch 12/15

19/19 [==============================] - 0s 3ms/step - loss: 2.0685 - accuracy: 0.6865

Epoch 13/15

19/19 [==============================] - 0s 3ms/step - loss: 1.9093 - accuracy: 0.6865

Epoch 14/15

19/19 [==============================] - 0s 3ms/step - loss: 1.7673 - accuracy: 0.6865

Epoch 15/15

19/19 [==============================] - 0s 3ms/step - loss: 1.6325 - accuracy: 0.6898

 

import pandas as pd

import tensorflow as tf

 

csv_file = tf.keras.utils.get_file('heart.csv', 'https://storage.googleapis.com/applied-dl/heart.csv')

 

df = pd.read_csv(csv_file)

 

df.head()

 

df.dtypes

 

df['thal'] = pd.Categorical(df['thal'])

df['thal'] = df.thal.cat.codes

 

df.head()

 

target = df.pop('target')

 

dataset = tf.data.Dataset.from_tensor_slices((df.values, target.values))

 

for feat, targ in dataset.take(5):

    print('Features: {}, Target: {}'.format(feat, targ))

 

tf.constant(df['thal'])

 

train_dataset = dataset.shuffle(len(df)).batch(1)

 

 

def get_compiled_model():

    model = tf.keras.Sequential([

        tf.keras.layers.Dense(10, activation='relu'),

        tf.keras.layers.Dense(10, activation='relu'),

        tf.keras.layers.Dense(1)

    ])

 

    model.compile(optimizer='adam',

                  loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),

                  metrics=['accuracy'])

    return model

 

 

model = get_compiled_model()

model.fit(train_dataset, epochs=15)

 

inputs = {key: tf.keras.layers.Input(shape=(), name=key) for key in df.keys()}

x = tf.stack(list(inputs.values()), axis=-1)

 

x = tf.keras.layers.Dense(10, activation='relu')(x)

output = tf.keras.layers.Dense(1)(x)

 

model_func = tf.keras.Model(inputs=inputs, outputs=output)

 

model_func.compile(optimizer='adam',

                   loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),

                   metrics=['accuracy'])

 

dict_slices = tf.data.Dataset.from_tensor_slices((df.to_dict('list'), target.values)).batch(16)

 

for dict_slice in dict_slices.take(1):

    print(dict_slice)

 

model_func.fit(dict_slices, epochs=15)